Lukáš Hanko’s research while affiliated with Slovak University of Technology in Bratislava and other places

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Publications (5)


Fig. 13 Actual by predicted plot
Comparison of the Regression Method and The Neural Network Method in Specific Cases of Engineering Practice
  • Article
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April 2025

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1 Read

Strojnícky časopis - Journal of Mechanical Engineering

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Lukáš Hanko

The presented article presents a summary of research findings based on a comparison of two statistical methods, both of which were applied to the same problem. These were data that were measured during the biomass compaction process, with softwood used as the input material. The aim of the analysis was to create models for both selected statistical methods that would sufficiently describe the given process and subsequently compare them. The second very important step was to compare the applicability of these methods from the point of view of limiting criteria and to define their advantages and disadvantages in practical use. For the purposes of the experiment itself, the biomass pressing process was used using a uniaxial press. In the case of this process, parameters were defined that significantly affect the quality of the resulting press, which is represented by the measured quantity - the press density. The parameters already mentioned, which by their nature significantly affect the quality of the press, are the pressing pressure, pressing temperature, humidity of the pressed material and the size of the pressed material fraction. The process itself and the subsequent experimental obtaining of results from the given research took place in laboratory conditions using an experimental uniaxial press. Subsequently, through the steps of statistical analysis, we carried out estimates of effects, individual tests of hypotheses about the significance of the model and effects and, last but not least, also the predictive ability of the models thus obtained. The obtained models, which were created using the two methods already mentioned, were subsequently compared based on their predictive ability, specifically through the so-called predictive ability of the model, which is represented by the coefficient of determination R2, which is defined as the ratio of the sum of squares SSM explained by the model to the total sum of squares SST. As a result, it expresses the degree of agreement of the observed values with the model. Based on the experimental knowledge thus obtained, the aim is to point out the significance and importance of statistical methods and methodologies that are useful in processing data of various nature and scope. It is precisely the modelling of a process, which is often complex in nature or demanding on the data obtained, that needs to be differentiated in the statistical methods used. The research results presented in the paper demonstrate their significance.

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Fig. 4 Simulated working range IRB 1100/0.58 with spindle a) 2D b) 3D
Fig. 8 Visualization of the horizontal (blue) and vertical (green) machining planes for experimental research
Overview of torques for position raL 1 Table 3 Overview of torques for position raL 2
Parameters of upcoming experimental design
Simulation of the Workspace and Manipulability of An Industrial Robot For Research in Robotic Milling Needs

April 2025

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2 Reads

Strojnícky časopis - Journal of Mechanical Engineering

Ján Kijovský

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Lukáš Hanko

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Jozef Bábics

The paper presents a findings in simulation of the workspace and manipulability of a robotic arm with a spindle. Workspace and manipulability simulation is an important step in next implementation into production cell and it can easily improve CAD and layout design of the production line. Authors would like to present also the torques calculation of individual axes of robotic arms. Presented activities are essential condition for the upcoming experimental design which will be executed in our institute. Goals of research activities on our institute is to determine the functional dependencies between technological parameters of robotic milling, and rigidity of robotic arm, which can be easily evaluated by final machined surface quality and precision of machined components. The paper covers mechanical and simulation system of the existing stand which includes the ABB IRB 1100/0.58 robotic arm.



Fig. 2 Mechanism with 15 degrees of freedom Such a 15 DOF vehicle model represents an actual physical SimRod vehicle. It is therefore necessary to assign values and parameters of this physical vehicle to the virtual model. These input parameters are: • mass and moments of inertia:
Fig. 9 Lateral forces on the wheels at a speed of 15.1 m/s The values of the lateral forces at the moment when the vehicle has lost stability on the road are given in Tab. 3. Tab. 3 Values of the lateral forces in the wheels during loss of stability at a speed of 15.1 m/s Wheel Lateral force (N) Front right 1806.1 Front left 898.5 Rear right 1747.5 Rear left 499.6 The centrifugal acceleration of the vehicle at 15.1 m/s was 7.4 mí µí± -2 . To calculate the centrifugal force: í µí°¹ í µí±¦ = í µí±š í µí±Ž = 680 . 7.4 = 5032 í µí± í µí°¹ í µí±¦ > í µí°¹ í µí°¹í µí± + í µí°¹ í µí°¹í µí°¿ + í µí°¹ í µí± í µí± + í µí°¹ í µí± í µí°¿ 5032 > 1806.1 + 898.5 + 1747.5 + 499.6 5032 í µí± > 4951.7 í µí±
Fig. 11 Lateral forces on the wheels at a speed of 11.4 m/s The values of the lateral forces at the moment when the vehicle has lost stability on the road are given in Tab. 5. Tab. 5 Values of the lateral forces in the wheels during loss of stability at a speed of 11.4 m/s Wheel Lateral force (N) Front right 762.8 Front left 552.6 Rear right 809.7 Rear left 387.2 The centrifugal acceleration of the vehicle at 11.4 m/s was 4.1 mí µí± -2 . To calculate the centrifugal force: í µí°¹ í µí±¦ = í µí±š í µí±Ž = 680 . 4.1 = 2788.1 í µí± í µí°¹ í µí±¦ > í µí°¹ í µí°¹í µí± + í µí°¹ í µí°¹í µí°¿ + í µí°¹ í µí± í µí± + í µí°¹ í µí± í µí°¿ 2788.1 > 762.8 + 552.6 + 809.7 + 387.2 2788.1 í µí± > 2512.3 í µí±
Conditions for Loss of Stability of an Autonomous Vehicle During a Cornering Manoeuvre

November 2022

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248 Reads

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2 Citations

Strojnícky časopis - Journal of Mechanical Engineering

This paper deals with the method of creating a virtual autonomous vehicle dynamics model SimRod in AMESim environment and implementing such a model in Prescan and MATLAB/Simulink environments and performing co-simulation of the vehicle cornering maneuver at different values of the grip factor in these software. As a result, the driving dynamics of the SimRod vehicle is assessed in the simulation environment and the stability of the vehicle in the turn with respect to the grip factor and vehicle speed is analyzed.


Citations (1)


... Processing occurs across IoV layers, with edge computing addressing the limitations of centralized cloud processing by reducing latency and congestion [15,16]. This proximity to data sources enables efficient handling of the large volumes generated by vehicles [17]. ...

Reference:

Edge to Cloud Task Offloading Optimization in Internet of Vehicles Networks
Conditions for Loss of Stability of an Autonomous Vehicle During a Cornering Manoeuvre

Strojnícky časopis - Journal of Mechanical Engineering